RT Journal Article
SR Electronic
T1 Probabilistic forecasting in infectious disease epidemiology: The thirteenth Armitage lecture
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 104000
DO 10.1101/104000
A1 Held, Leonhard
A1 Meyer, Sebastian
A1 Bracher, Johannes
YR 2017
UL http://biorxiv.org/content/early/2017/01/30/104000.abstract
AB Routine surveillance of notifiable infectious diseases gives rise to daily or weekly counts of reported cases stratified by region and age group. From a public health perspective, forecasts of infectious disease spread are of central importance. We argue that such forecasts need to properly incorporate the attached uncertainty, so should be probabilistic in nature. However, forecasts also need to take into account temporal dependencies inherent to communicable diseases, spatial dynamics through human travel, and social contact patterns between age groups. We describe a multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions. Probabilistic forecasts of the total number of cases can be derived through Monte Carlo simulation, but first and second moments are also available analytically. Final size forecasts as well as multivariate forecasts of the total number of cases by age group, by district, and by week are compared across different models of varying complexity. This leads to a more general discussion of issues regarding modelling, prediction and evaluation of public health surveillance data.